Landslide hazard and risk zonation remain challenging due to the complexity of quantifying risk over large areas. While risk assessment is feasible at site scales, generating quantitative risk maps for local authorities at medium scales (1:10,000–1:50,000) is still difficult. This paper discusses the challenges in quantifying landslide risk, including the creation of landslide inventory maps with details on date, type, and volume of landslides, determining spatial and temporal probabilities, modeling runout, and assessing vulnerability. Recent developments in medium-scale hazard and risk zonation are reviewed, highlighting new advances such as the use of detailed topographic data, event-based inventory maps, spatial-temporal probabilistic modeling, and deterministic modeling with land use and climate change scenarios. Landslide risk is defined as the expected loss of lives, injuries, property damage, and economic disruption due to landslides. It is calculated as the product of vulnerability, amount at risk, and the probability of the event. Total risk is the sum of hazards multiplied by expected losses for all elements at risk. The formula for risk is: Risk = Σ(HΣ(VA)), where H is hazard (probability of occurrence), V is vulnerability, and A is the amount or cost of elements at risk. Calculating consequences (VA) for all elements at risk is complex, requiring consideration of many factors. When scaling up from detailed site investigations to risk zonation maps, it is difficult to accurately identify elements at risk and landslide locations. The process involves analyzing spatial and temporal probabilities of mass movements affecting different elements at risk. A GIS-based approach for medium-scale risk assessment requires environmental factors, triggering factors, historic landslide data, and elements at risk. Historic landslide data is crucial for understanding frequency, types, volumes, and damage. Landslide inventory maps, derived from archives, field data, interviews, and images, are essential but often incomplete, making quantitative risk assessment difficult. Triggering factors, such as earthquakes and rainfall, must be converted into magnitude-frequency relations, requiring detailed geotechnical data for accurate modeling.Landslide hazard and risk zonation remain challenging due to the complexity of quantifying risk over large areas. While risk assessment is feasible at site scales, generating quantitative risk maps for local authorities at medium scales (1:10,000–1:50,000) is still difficult. This paper discusses the challenges in quantifying landslide risk, including the creation of landslide inventory maps with details on date, type, and volume of landslides, determining spatial and temporal probabilities, modeling runout, and assessing vulnerability. Recent developments in medium-scale hazard and risk zonation are reviewed, highlighting new advances such as the use of detailed topographic data, event-based inventory maps, spatial-temporal probabilistic modeling, and deterministic modeling with land use and climate change scenarios. Landslide risk is defined as the expected loss of lives, injuries, property damage, and economic disruption due to landslides. It is calculated as the product of vulnerability, amount at risk, and the probability of the event. Total risk is the sum of hazards multiplied by expected losses for all elements at risk. The formula for risk is: Risk = Σ(HΣ(VA)), where H is hazard (probability of occurrence), V is vulnerability, and A is the amount or cost of elements at risk. Calculating consequences (VA) for all elements at risk is complex, requiring consideration of many factors. When scaling up from detailed site investigations to risk zonation maps, it is difficult to accurately identify elements at risk and landslide locations. The process involves analyzing spatial and temporal probabilities of mass movements affecting different elements at risk. A GIS-based approach for medium-scale risk assessment requires environmental factors, triggering factors, historic landslide data, and elements at risk. Historic landslide data is crucial for understanding frequency, types, volumes, and damage. Landslide inventory maps, derived from archives, field data, interviews, and images, are essential but often incomplete, making quantitative risk assessment difficult. Triggering factors, such as earthquakes and rainfall, must be converted into magnitude-frequency relations, requiring detailed geotechnical data for accurate modeling.